Dioxin emission concentration prediction method

Abstract

The invention discloses a dioxin emission concentration prediction method based on hybrid integration of a random forest and a gradient boosting tree, and the method comprises the steps: firstly carrying out random sampling of a training sample and an input feature for DXN modeling data with a small sample high-dimensional feature, so as to generate a training subset; then, establishing J DXN sub-models based on RF on the basis of the training subsets; then, carrying out I times of iteration on each RF-based DXN sub-model, and constructing J * I GBDT-based DXN sub-models; and finally, combining the prediction outputs of the DXN sub-models based on the RF and the GBDT by adopting a simple average weighting mode to obtain a final output. By adopting the DXN prediction model construction method integrating RF and GBDT, the DXN online prediction precision can be improved, the operation optimization of MSWI process operation parameters is assisted, and the economic benefits of enterprises are improved.

Description

technical field [0001] The invention belongs to the technical field of urban solid waste incineration, and in particular relates to a dioxin emission concentration prediction method based on random forest and gradient boosting tree hybrid integration. Background technique [0002] The rapid economic development and the continuous upgrading of urbanization have led to a rapid increase in the generation of municipal solid waste (MSW) in my country, especially in economically developed and densely populated areas, and some cities are facing a crisis of garbage siege [1]. Municipal solid waste incineration (MSWI) power generation is a typical treatment method to achieve waste reduction, recycling, and harmlessness [2]. At present, the number of MSWI power plants in China has exceeded 300, and grate furnace incinerators account for more than 2 / 3 [3]. Due to the particularity of waste components in my country, most of the imported incineration equipment is in the state of manual ...

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Problems solved by technology

[0003] In the actual industrial process, the method of combining online sampling and offline experimental analysis is mainly used to detect DXN emission concentration according to a certain period [3], but this method is expensive and the period is relatively long. The main problem is: it is difficult to support MSWI operating parameters The real-time optimal control of DXN to achieve the goal of minimizing the emission concentration of DXN[7]

Most of the above studies use a single RF or GBDT algorithm for modeling, and it is difficult to effectively construct a DXN emission concentration prediction model with small samples and high-dimensional characteristics.

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